Li Jiang

The paper proposes Arbiter-K, a Governance-First execution architecture that treats LLMs as probabilistic units encapsulated by a deterministic kernel, significantly improving the security and reliability of agentic AI systems.

The paper introduces a semantic validation framework that uses unpackers as executable contracts to detect and repair semantic bugs in packer identification tools, significantly improving the reliability of malware analysis.

LoopFM proposes a novel framework to significantly improve knowledge distillation for recommendation systems by structuring the rich intermediate embeddings of large foundation models as input features, thereby overcoming the limitations of single-scalar prediction transfer.